The FCN provides class labels for each point of the laser range scan. We perform highly efficient processing of the point cloud by first using a spherical projection.
Classification results on this two-dimensional spherical projection are then back-projected to the three-dimensional point cloud.
However, the back-projection introduces artifacts, which we reduce by a two-step process of an erosion followed by depth-based flood-fill of the semantic labels.
The semantic labels are then integrated into the surfel-based map representation and exploited to better register new observations to the already built map.
We furthermore use the semantics to filter moving objects by checking semantic consistency between the new observation and the world model when updating the map.
In this way, we reduce the risk of integrating dynamic objects into the map.
Fig. REF  shows an example of our semantic map representation. The semantic classes are generated by the FCN of Milioto et al.  [1]}, which was trained using the SemanticKITTI dataset by Behley et al.  [2]}.
